Synching with seasonality: Predicting roe deer parturition phenology across its distributional range.

Latitude and elevation are the most commonly studied drivers of large-scale variation in the phenology of life-history events. However, these coarse gradients cannot reliably predict observed spatial variation in phenology. Although it is less often investigated, environmental predictability is also a selective force that constrains spatial variation in life-history events. Here, we explore how environmental predictability contributes to shaping spatial variation in the parturition phenology of roe deer across its distributional range in Europe. We compiled data on roe deer parturition dates across Europe within the research collaboration EURODEER, and from citizen scientists and related birth dates to elevation and environmental predictability, measured by Colwell's metrics of contingency and constancy, based on high-resolution climate and NDVI values. We predicted parturition timing and synchrony simultaneously within a single modelling framework using a distributional regression model (i.e. GAMLSS). Our approach provided more robust predictions of variation in birth phenology than commonly used approaches based on the combination of latitude and elevation. We found that roe deer align their parturition dates with both elevation and seasonality in environmental conditions. We also identified an apparent shift towards later parturition from west to east across the distributional range in Europe, putatively linked to relatively milder and more constant climates in the west. Contrary to our expectations, we did not find any consistent link between parturition synchrony and environmental predictability, suggesting that other factors, such as small-scale heterogeneities in landscape composition, play a key role. Our work emphasizes the importance of understanding macrophenological processes in the variation of life-history event timing across space. It also highlights the need to account for this spatial variation when investigating region-specific adaptations, particularly in light of climate change.